Ideas Lab

No automated phone agent for handling calls and goals

Users need an AI phone agent that can take defined goals like disputing bills or booking appointments, navigating phone trees and holds autonomously until resolution.

X3mo agoToolAI

Discovered Materials (YC P26) – AI agents to discover new materials

Hey HN, we're Advaith and Akash from Discovered Materials ( https://discoveredmaterials.com/ ). We build AI agents that discover new materials for the semiconductor industry.GPUs today have a heat problem. Nvidia & AMD are almost doubling the TDP (Thermal Design Power) in every chip they release - the H100 (released 2022) has a TDP of 700W, Blackwell (2024) gives out 1.2 kW and Rubin (2026) gives out at 2.3 kW of heat. This trend is expected to continue, and getting rid of this heat is one of the major reasons datacenters consume so much power and water today - they need it to keep chips cool during operation.The amount of heat produced by a chip and its ability to dissipate it are both influenced by the materials used to make it. For example, we could reduce the energy per bit required to move data between logic and memory by 10-50x by 3D packaging chips (placing HBM memory stacks directly on top of logic chips, instead of placing them beside logic on a 2D circuit board). However, we're unable to do this today because the dielectric material used in HBM (such as SiO2) is a very poor thermal conductor, trapping heat between logic and memory and causing drastic temperature rise during operation. Similarly, there's many other materials in the GPU that are being re-evaluated today - 2 more examples are thermal interface materials and substrates. However, getting a new material into a fab takes years and hundreds of millions of dollars of research - the infamous "lab-to-fab valley of death".At Discovered Materials, we're optimistic that AI agents can reduce the timeline and cost required to introduce new materials into semiconductor chips. We're seeing glimpses of this already - we tested 7 models from Anthropic, OpenAI and Kimi, and found that they're all able to computationally discover new materials that are dynamically stable and possess promising properties. This was surprising to us - it would generally t

Hacker News2mo agoToolAI

Keet (YC S24) – An app to create video courses on anything

Hi HN! We’re Zack and Tommy the Co-Founders of Keet (https://trykeet.com). We are building a mobile app that generates courses on any topic, with short videos for explanation and games for reinforcement. Courses mirror a real curriculum to help you learn over an extended period of time.Tommy and I met in linear algebra class in college and spent the next 4 years taking classes together. Our friendship was formed around learning new things. In school, someone else designs the curriculum, delivers the content, and writes the assessments. All you have to do is show up. Learning without this structure was frustrating, and there was a lot of friction to get started.Zack has a coffee obsession and struggled to assemble youtube videos, conversations with ChatGPT, and the books he was reading into a coherent understanding of all of the different variables that go into brewing a cup of coffee. His attempts gave him the freedom to follow his curiosity, but the instructional design was difficult. Every piece of content either presumed some prerequisite knowledge or none at all.Keet is our attempt at providing the autonomy to teach yourself anything while adding a structure conducive to learning. It finds a custom starting point and sequences lessons in a logical order.We have found Keet most useful in the following scenarios:- You have a subject matter interest that you enjoy passively learning about. (i.e. you really enjoy learning about medieval history and generate courses on medieval engineering)- You want to explore a niche topic of a subject area you already know a lot about. (i.e. You know a lot about biology but want to explore how migratory animals sense Earth’s magnetic field.)- You see a really niche topic get mentioned somewhere you want to explore more (i.e History of Penny Universities, Double Entry Book keeping or Robert Moses and the creation of the BQE)When you create a course we ask some questions about how difficult it should be, how much depth the

Hacker News2mo agoToolAI

In your experience, what are sound conventions for e-ink UI development?

TL;DR I'm looking for advice regarding browser-based frontend development from people with practical e-ink UI experience. My specific device is a Bigme Hibreak Pro BW but I'm aiming for relatively broad compatibility.I've recently switched to a black-and-white e-ink smartphone with the motivation of withdrawing from the attention economy somewhat and it's a genuinely cool piece of hardware. While the majority of my needs are met by this device there are a few things I'd like to have which don't work terribly well with the e-ink screen. I'm planning to implement a couple of projects to fill these gaps, at the moment I'm planning a Lemmy frontend and an OpenRouter frontend specifically for e-ink. Both are to be browser-based rather than native, to maximise compatibility and because I'm much more familiar with the web than Android development.I would like to study the principles of sound e-ink UI design before approaching these projects to avoid creating unusable slop, in particular I am not entirely sure how to approach treating the refreshes as a first-class aspect of the design when I can't control them from the browser, and how to apply comprehensible UI conventions when a greyscale, high-contrast display is the target.Some specific problems I have out of the gate are:* Streaming LLM output to the screen is basically the worst-case scenario for e-ink, I need to buffer it and paint it in chunks without this becoming horrible to use.* Ghosting is a serious problem, browsing HN on the device is a particularly obvious example. Ideally I want to avoid scrolling as far as possible and rely on pagination instead, which I feel has the potential to become annoying if not done well.* Given I must rely exclusively on layout and type to carry the UI, what design languages emphasise these qualities best? My gut says the early Mac OS versions wouldn't be a bad place to start, this seems relevant given the display constraints

Hacker News2mo agoArticleAI

28FL score

Stoa Markets (YC S26) – A Marketplace for GPUs and AI Servers

Hi HN, we’re Eren, Berat and Kaan. We’re building Stoa (https://www.stoaexchange.com), a marketplace for new and used GPUs and AI servers.GPUs are the collateral in the data center buildout. Today, financing terms mostly depend on the offtaker, meaning the company that has committed to use the compute.If that company is a hyperscaler, the financing can look investment grade. If it’s a smaller cloud or startup, terms get expensive fast, even with the same hardware as collateral.The lender’s problem is pretty reasonable. If the borrower defaults and we need to sell these servers, what can we actually get for them? There isn’t a good answer today.We started brokering GPU deals to understand why. It was much more manual than we expected. The hardware is still traded through phone calls, forwarded spreadsheets and long email threads.One week, a seller quoted us $200k for a server node and another quoted $240k for what looked like the same thing. Neither was necessarily wrong. They had different information and could only see their own corner of the market.Before we could compare the quotes, we had to sort out the configuration, condition, warranty, location and delivery terms. It’s the same information Kelley Blue Book attaches to a used-car price through the year, trim, mileage and condition. “An H100 server” isn’t enough information to know what something is worth, just as “a used BMW” isn’t.This is also just a bad way to buy or sell hardware. A buyer looking for the best price shouldn’t have to contact several brokers and dealers separately, repeat the same request and then untangle a pile of different quotes. Sellers shouldn’t have to search for demand one buyer at a time. A market of this size deserves better liquidity.Stoa puts the request into one format and sends it to dealers that have gone through know-your-business (KYB) checks. We verify the company, who owns it and who is allowed to trade for it. Before the request goes out, the buyer confirms the

Hacker News2mo agoToolAI

Needle2: 14MB agentic LLM for phones, wearables, smart home and robots

Hey HN,Henry from Cactus here!We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2.The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges 300-700 on sub-$200 phones such as the Samsung A-Series.On the tool call and mobile device use benchmarks, Needle 2 trades wins with closest small models like LFM2.5 230M and Apple Foundation Model, at 5x to 70x smaller, both at f16 vs Needle 2 at 2bit. Needle is based on Simple Attention Networks from our paper (https://arxiv.org/abs/2607.18363).Edge AI has lately meant Macs and PCs, but that is just 1.5 billion of over 21 billion connected IoT devices in the world today, and in emerging markets most phones ship under $200, no NPU, cheap GPUs. These include budget phones, Raspberry Pis, microcontrollers, wearables, small robots like Reachy Mini, and connected home devices.A conventional transformer of Needle's width and depth spends 164 MFLOPs per token, and even one squeezed down to Needle's parameter count spends 87, Needle spends 70. Even on a high-end phone, an always-on assistant lives inside a power budget; every MFLOP is milliwatt-hours, and Needle spends 7x to 85x fewer of them per token than the smallest performant LLMs. More about the architecture in the link.When we structure intelligence for consumer devices as functions with typed parameters, the only hard part is mapping a messy sentence onto them; which function, with which values. Our research found that when framed that way, the problem needs no world knowledge and no open-ended prose

Hacker News2mo agoToolAI

Airy – Free, fast, and simple voice content creation

Hacker News2mo agoToolAI

42FL score

A terminal glued to the macOS dock

Every single day of my life since I started programming back in 2020 I open my projects through the terminal/ghostty. "cd path" + "code .". Until today I don't even know how to open vscode first then open a project from there.I usually have 3-4 vscode windows open at the same time, and I like them in separate desktops (for mac users). i gotta admit, i use 8 desktops, 3-4 for vscode, 1 for arc, 1 for docker, 1 for postman etc.So my daily routine when I turned on my mac was: (1) open ghostty; (2) cd-path+code.; (3) drag the vscode window to a desired desktop; (4) get back to the ghostty desktop; (5) repeat until all is open.i was unbothered for 6y. Until i realized how inefficient i was being: (1) opening ghostty just to run one-off commands to open another app felt wrong, like why do I need a terminal if it's not a process I need to watch; (2) the need to go back to the ghostty desktop every time i wanted to open a new project and drag it to a new desktop and switch back... it's funny how we simply accept a habit and don't bother to fix iti wanted a terminal that persisted across desktops AND that was always visible so i could access it anytime AND that wasn't big because i would only run one-off commands. so I created starboard.where could I fit a terminal that was always visible and accessible? i looked at the macOS dock, i looked at the empty space to its right, and I said "that's it".starboard is a floating terminal window that's literally glued to the dock, filling the blank space to the right, tracking its height and position live. it's visible from any desktop, a real companion to the dock, and it's a persistent shell.to close the story on my routine, I now can happily jump straight to the desktop i want my project to be in and open it there through Starboard. i rarely open ghostyy, unless i really need it.here's where feedback is massively important. i built this for the way

Hacker News2mo agoToolAI

42FL score

Wallfacer – A terminal session manager for Claude Code, and more

Hacker News2mo agoToolAI

ProvenMetal (YC S26) delivers circuit boards in days instead of weeks

Hey HN, we’re Will & Johnny from ProvenMetal (https://provenmetal.com). You send us design files or specs and we give you assembled boards domestically in days.The US produced 30% of PCBs globally in 2000, now they produce 4%. Chinese manufacturers have completely dominated this space at 55% of global production.Now, the need for a domestic PCB supply chain is higher than ever before, yet the infrastructure has been dissolving over the last 2 decades. What is left is mostly small family run manufacturers (CMs) that have been operating in largely the same, labor intensive way since the early 2000’s.When you place an order through a CM it typically takes several days to receive a quote and complete design for manufacture review, and then you have to source all of the components (the hardest part) and bare boards yourself, then wait anywhere from a few days to several weeks for the assembly and testing of those boards.We started off assembling circuit boards out of a garage with prosumer grade equipment (NeoDen YY1, Glenbrook X-ray, solder paste stencils, and manual rework stations). We believed that by owning the manufacturing process, we could turn all the front of house automations inwards. But here’s the thing… manufacturing circuit boards with prosumer grade equipment out of a garage takes a huge amount of time. Suddenly we were spending 90% of our time assembling circuit boards instead of growing the business. We were completely capacity constrained, with fancy software automations that are not the binding constraint at low volumes.Then we got our heads out of the weeds, took a step back, and realized that we were trying to solve the wrong bottlenecks.These manufacturers are good at manufacturing and terrible at the front of house (quoting, DFM review, and part procurement). When you look at the full process, you realize that assembly is not the bottleneck. So we stopped trying to solve the problem that we can’t solve at this stage.We measured the b

Hacker News2mo agoToolAI

I spent 2 years designing a mechanical Magic Keyboard

Hi HN, today I’m revealing Altar II, an ultra-low profile (4.75mm) mechanical keyboard designed to replace your Magic Keyboard. It’s the keyboard I wish Apple made.Altar II has fully mechanical switches, plus a magnetically detachable dial, haptic feedback, a red backlight, and a native macOS companion app.I’m a heavy Mac user and I feel that Apple hasn’t given its keyboards the attention they deserve, in terms of hardware, software, and features, so I set out to try and change that.It was a massive undertaking and it has taken almost 2 years to get to this stage. One of the main obstacles was fitting a reasonably sized battery into the chassis, and aggressive power profiling without messing with the user experience. Another issue was we couldn’t fit any components on the underside of the PCB because there was literally no space to do so (and there are a lot of components).Around 4 years ago I posted my first keyboard to HN (https://news.ycombinator.com/item?id=32507172). Its response blew me away and ultimately led to me quitting my job and making and selling keyboards full time. I’ve taken some of the feedback from that original post and incorporated it into Altar II (e.g. the detachable dial).I’ll be around to answer any questions. Please let me know what you think, I’d love to get some feedback.

Hacker News2mo agoToolAI

HyperProbe (YC S26) – Agents that do read-only debugging in prod

Hi HN, this is Shailendra and Karan here. We are building a fast and safe way for coding agents to debug issues live in production.When prod breaks, it lets Cursor, Claude, and others drop virtual breakpoints or probes safely in your running code, and extract the exact variable values that logs don’t have.All this saves time and effort for engineers who’d otherwise dig through logs and traces or redeploy with console.logs or print statements until they find the root cause.Here is the link to the video that explains this: https://www.youtube.com/watch?v=ivV7I--ta5cAgents write most of our code now. This shrinks the useful context engineers need to debug AI written code, a problem not helped by the limited telemetry added in the same code by AI.So when something breaks in prod, the first instinct for an engineer is to open logs or throw them to your agents. But if the line you are looking for is not there, agents will start guessing the root cause on non-existent data, forcing you to add a log, and redeploy.This analysis-inference loop of agents with existing data does not come cheap, burning a lot of tokens. And the add log, redeploy cycle is so slow and painful that it makes engineers hate on-call.Our approach lets agents capture telemetry on-demand at the exact moment and point of failure, killing the log-redeploy cycle and getting the most accurate RCA while burning fewer tokens.The obvious problem is making it work on a running service. You can't pause a live service the way you'd pause a debugger on your laptop. Getting the value out of a running process safely, without pausing a thread or slowing the host is the challenge.We are making this happen.Before this I ran engineering at a 100 member team. Then Karan and I spent three years on HyperTest which was a testing tool.At HyperTest, we turned production traffic into integration tests using OpenTelemetry. That was production instrumentation too. The hard parts of pulling real runtime st

Hacker News2mo agoToolAI

Nightcrawler – A local AI pentesting agent running on a smartphone

Hacker News2mo agoToolAI

35FL score

Hacker News with AI stories filtered out

Hi folks,I've noticed that a lot of people here seem exhausted by the amount of AI news on the front page. I shared hcker.news here a year ago and it has since gained a ton of filtering features, including a dedicated AI filter, so I figured I should tell more people about it.The filter works in three passes: 1. Known AI-related keywords and domains are filtered automatically. 2. An agent reviews the remaining articles and removes those it identifies as AI-related. 3. I make the call on uncertain cases. You can also exclude GitHub repos posted to HN that show signs of AI authorship. The filter checks: - Commit messages for agent attribution - Contributors for known coding agents - Repository files for agent instructions and configs No filter will be perfect, but this is as good as it gets. lmk what you think. Cheers.More links:Original Show HN: https://news.ycombinator.com/item?id=44075353If you only want AI stories: https://hcker.news/?view=frontpage&ai=includeRSS: https://hcker.news/feeds/atom?period=day&ai=excludeA github-only non-AI feed: https://hcker.news/?ai=exclude&include_domains=github.com

Hacker News2mo agoToolAI

EdotEnv (YC S26) – Quant Trading RL Envs to Teach LLMs Research

We are Rui and Michael and we’re building EdotEnv (https://edotenv.com): self-improving RL environments from Quant Trading workflows.With all the benchmaxxing around, evals saturate and become meaningless for model comparison. Useful benchmarks should increase in difficulty as models advance. Back in our Quant jobs, Michael and I saw that the market has exactly this property: markets became more efficient as people profited from trading inefficiencies, making new profitable strategies harder to find and old ones decay over time.This makes markets an ideal, continuously evolving benchmark for LLM training. The hard part is to turn professional quant workflows into reliable training envs, as this is a very niche expertise.In our environments, we give LLMs a quant trading workflow and evaluate their performance on out-of-sample data: build predictive features/ models, design a portfolio, backtest strategies, adapt continuously to market regimes. Each step is a task with different self-built tools. For example, a predictive feature building task gives the agent cleaned market data of time period [0,T] to research ideas, a backtesting tool to test created features at time t on [0, t], an execution tool to trade strategies with the new features on [t+1, T] and a final evaluation. Our reward isolates the agent's feature building skills and yet benefits from market properties.From running SOTA models in our environments, we see that i) they seem to struggle with iterating deeply on research ideas, preferring broad shallow searches; ii) higher reasoning does not seem to increase performance and iii) agents do not understand trading, e.g. when losing money they stop trading instead of trading smarter. Check out our blogs for more details! https://edotenv.com/?tab=blogQuant workflows are essentially applied ML research, long-horizon planning and continual learning. Through our envs, we teach these transferable research skills, rather than tas

Hacker News2mo agoToolAI

Hoplite (YC S26) – Effortlessly deploy cloud coding agents

Hi HN, we’re Bence and Ryan, founders of Hoplite (https://hoplite.sh). Hoplite lets you deploy coding agents in the cloud, with a suite of tools that makes it incredibly easy to QA features. During onboarding, we port over your local setup - sessions, memories, MCP servers, and get your projects ready to run in the cloud.Here’s a demo: https://youtu.be/bnyktZ_9pjEWe got here after pivoting away from the idea we applied to YC with; AI for retail investing. It ultimately wasn’t a product that we ourselves would use, nor served a customer base that we felt connected to. In reflecting on what we really wanted to do, we realised that we loved talking to founders and developers, and were really opinionated about the specific area of cloud agents. We tried out all the existing solutions, and didn’t find one that A) took good advantage of being in the cloud, and B) was performant and felt good to use.We’re building a product that we feel reflects what mainstream development will look like in 6-12 months. As models improve, developers will end up reviewing less and less code, and will instead focus on reviewing the product output. That means evaluating new user flows, visually verifying that new features look good, that the API works as expected, that the CLI works on Windows, etc. And doing it while running hundreds of agents concurrently.On the agent side, we’ve created a custom harness. We spent a lot of time deciding on whether we should use an off the shelf solution like Codex/Claude Code, but ultimately wanted the independence and freedom that came with building it in house. It also means that we can test out completely new features without relying on Anthropic and OpenAI to catch up.Everything is hosted on AWS, with the exception of: Temporal for durable workflows, Modal for sandboxes, and Planetscale for our database. Our infra decisions were driven by a strong belief that agents are becoming a tier 0 piece of infrastructure, and they ne

Hacker News2mo agoToolAI

AI image generation tools over-moderate innocent prompts

Users of Grok Imagine face excessive moderation and blocks on fully clothed, non-explicit prompts, despite seeing unmoderated explicit content elsewhere on the platform.

X2mo agoToolAI

35FL score

Vimgolf.ai – Learn Vim by playing through a map of levels

Just launched my app, VimGolf.ai! As users complete the levels on the map, I introduce progressively more complicated concepts — it’s aimed at people who have never used Vim. Any feedback would be much appreciated!

Hacker News3mo agoToolAI

What should the GUI for AI agents look like?

Hi HN! We’re Akilan and Miguel, the creators of MarbleOS.The inspiration for Marble comes from the GUI work at Xerox PARC, the 1984 Macintosh, and later NeXTSTEP, which became the foundation for Mac OS X. Before GUIs, interacting with a computer was limited to strange terminal commands:C:\> DIRC:\> COPY FILE.TXT A:You had to remember the command, syntax, paths, and parameters.The GUI made those capabilities visible. Instead of remembering commands, you could point at files, drag them, click buttons, and select actions from menus. It didn't necessarily make entirely new things possible; it just made existing capabilities much easier to understand and use. We feel like AI is still somewhere around this command-line stage.Even though the strict syntax has been replaced with natural language, the interaction can still be quite stiff and depend on heavily recall. Tools like Claude Cowork still look surprisingly terminal-like: /skill-name [param1] [param2]. The parameters are written in natural language, but the user still needs to know the capabilities that exist, and how to invoke it. Command-line flags and arguments have just been replaced with tools, skills, and context.ChatGPT works very well for asking questions, but we aren't convinced that it's the final interface for delegating work across multiple agents. A blank text box and a list of chat threads feels limiting. An agent's capabilities are mostly invisible. With Marble, we're exploring an interface that treats AI more like a workspace than another chat app. Each delegated task becomes a card. Multiple jobs can sit next to each other and run at once. Files, tools, and finished artifacts are visible at once. Before a task runs, Marble also shows which tools it expects to use. The basic idea is that the user should not have to hold the entire structure of the task in their head. And the result should be something directly usable, like a spreadsheet, PowerPoint, or other file, r

Hacker News3mo agoToolAI